where \(\eta > 0\)  is, as before, the learning rate. In principle, all the different learning rate selection methods considered above can be applied to the Gibbs posterior framework to choose an appropriate value of \(\eta \) . Here, however, the loss function is not differentiable, which creates a challenge for the methods of [1]} and [2]}. Therefore, in what follows, we only compare GPC and SafeBayes.
